Ovarian follicles are fluid-filled ovarian structures that emerge during the ovulation period. A follicle that has certain physical characteristics may contain an oocyte. To increase the chance of fertility with in vitro fertilization procedures, it is important to identify such follicles and extract the oocytes at the right time to fertilize them in vitro. Ultrasound has been used for non-invasive detection of follicles, e.g., due to its ease of use, real-time imaging, ability to delineate follicles and affordability. Follicles that are ready for extraction appear larger both in diameter and in overall area. Therefore, clinicians often use ultrasound to measure follicle diameters and manually segment them to measure their cross-sectional area as a mean to characterize individual follicles. Unfortunately, this can be a painstaking task for clinicians given that ovarian ultrasound imaging may depict as many as twenty follicles. Performing measurement on each of the individual follicles can therefore be time consuming and prone to operator errors.
Automated approaches perform automatic measurement of follicle properties in ultrasound images. One approach involves determining inner and outer walls of a follicle through cost function optimization that takes into account image intensity and directionality of the wall at each point. Unfortunately, this approach may yield non-smooth follicle borders and is sensitive to accuracy of the edge detection technique. Another approach detects an inner and an outer border of a follicle and then takes into account a distance between adjacent follicles to optimally estimate the associated outer borders. However, these approaches may lack in measurement accuracy. Generally, follicles that are 15 to 20 millimeters (mm) in diameter are considered mature and are good candidates for egg aspiration. Accurate measurement of the diameters of follicles, several of which may be visible in a single transvaginal ultrasound image, is important for precise aspiration and staging of the follicle growth.